Text-like edges in digital images that are reproduced using, for example, color scanners and color printers, often are degraded by the presence of color fringes and other artifacts near the text-like edges. Scanned compound documents, which contain both images and text, are particularly susceptible to such degradation. The presence of these artifacts significantly degrades the overall appearance quality of the reproduced digital images. In addition, such degradation adversely affects the efficiency with which various compression algorithms may code digital images to reduce the amount of memory needed to store the digital images. For example, so-called “lossless” compression schemes generally do not work well on scanned images. So-called “lossy” compression methods, on the other hand, generally work well on continuous tone regions of scanned images but not on regions of scanned images containing text.
Compound documents may be compressed efficiently using a mixed raster content (MRC) document image representation format. In this compression scheme, an image is segmented into two or more image planes. A selector plane indicates, for each pixel, which of the image planes contains the image data that should be used to reconstruct the final output image. The overall degree of image compression may be increased in this approach because the image data oftentimes can be segmented into separate planes that are smoother and more compressible than the original image. Different compression methods also may be applied to the segmented planes, allowing the overall degree of image compression to be further increased.
One approach for handling a color or grayscale pixel map of a scanned compound document for compression into an MRC format involves segmenting an original pixel map into two planes and compressing the data of each plane. The image is segmented by separating the image into two portions at the edges. One plane contains image data for the dark sides of the edges, while image data for the bright sides of the edges and the smooth portions of the image are placed on the other plane.
Another approach for handling scanned document images includes an edge detector that detects edges of text in a digital image containing visual noise. A background luminance estimator generates a background threshold that is based on an estimation of the image background luminance. The background threshold depends on the luminance values of the edge pixels of the detected edges. In one embodiment, the background threshold is generated using only the edge pixels that are on the lighter side of the detected edges. An image enhancer at least partially removes visual noise in a scanned document by selectively modifying pixel values of the image using the background threshold. The image enhancer also may perform color fringe removal and text enhancements, such as edge sharpening and edge darkening.
Various unsharp masking approaches also have been proposed for sharpening edge features in digital images. In general, an unsharp mask filter subtracts an unsharp mask (i.e., a blurred image that is produced by spatially filtering the specimen image with a Gaussian low-pass filter) from an input image. In one approach, an unsharp mask filter increases the maximum local contrast in an image to a predetermined target value and increases all other contrast to an amount proportional to the predetermined target value. In an adaptive spatial filter approach, pixels of an input image with activity values that are close to an iteratively adjustable activity threshold are selectively enhanced less than the image pixels with activity values that are substantially above the threshold. In another spatial filtering method, an adaptive edge enhancement process enhances the sharpness of features in an image having steep tone gradients.